Hugo Tekeng

Academic overview

My academic path is built on two complementary degrees: a rigorous foundation in applied mathematics, followed by a specialization in computer science and data science.

University of DschangCameroonWES: Bachelor’s degree

Bachelor’s Degree in Applied Mathematics

2019 — 2023

Initial academic training in analysis, algebra, probability, statistics, differential equations, modeling, and scientific computing.

UQTRCanada2023 — present

Bachelor’s in Computer Science — Data Science

Final term in progress • seeking Summer 2026 internship

Further development in software engineering, data science, databases, systems, networks, architecture, and applied projects.

Path progression

Applied Mathematics → Computer Science → Data / Software / Platforms

This progression makes my profile especially coherent: a strong mathematical foundation for modeling and analysis, extended by advanced training in computing focused on systems, data, and real-world projects.

Filter by domain

All courses

Fall 2024

5 courses
INF1006Software

Analysis and Modeling

Course focused on the early phases of software development, especially requirements analysis, object-oriented modeling, and software specifications. It helped me understand how to transform business needs into structured models that support software design and implementation.

INF1035Object-Oriented Programming

Advanced Object-Oriented Concepts

Advanced object-oriented programming course focused on higher-level software design and development mechanisms. It allowed me to go beyond core OOP concepts by studying generics, design patterns, application robustness, event-driven programming, and advanced concepts such as design by contract and aspect-oriented programming.

PIF1006Mathematics

Mathematics for Computer Scientists II

Applied mathematics course for computer science covering advanced topics useful in several domains, including formal languages, automata, matrix algebra, numerical analysis, cryptography, and data compression. It helped me connect mathematical concepts to concrete computing implementation problems.

SDD1001Data

Introduction to Data Science

Introductory data science course focused on the use of modern languages and libraries to manipulate, analyze, and visualize data. It helped me build practical foundations in Python and R, work with data structures, use specialized libraries, and approach preprocessing and analysis tasks such as linear regression.

SIF1015Systems

Operating Systems

Introductory course on the fundamental concepts of operating systems, with a strong focus on systems programming and experimentation under open environments such as UNIX and Linux. It helped me understand how an operating system manages processes, threads, memory, files, I/O, and network communication, while developing concrete system utilities.

Winter 2025

5 courses
INF1007Software

Software Design

Course focused on the design of high-quality software, as a continuation of analysis and modeling. It helped me deepen my understanding of the design process, use modern techniques such as UML, design patterns, and software architecture styles, and complete a team project covering multiple stages of software development.

INF1008Algorithms

Algorithm Analysis and Design

Course focused on the theoretical analysis and design of efficient algorithms. It helped me deepen my understanding of complexity, compare several algorithmic strategies, and choose the most appropriate methods depending on the problem, while considering correctness, efficiency, and application context.

INF1009Networks

Computer Networks I

Introductory course on computer network architectures, focused on the mechanisms that enable data exchange between computers. The course helped me understand layered architectures, communication protocols, local area networks, virtual circuits, and the fundamentals of IP networking.

SDD1002Data

Modeling and Simulation

Course focused on theoretical and practical techniques for modeling, simulation, and data analysis. It allowed me to work across several stages of a data science pipeline: data collection, cleaning, preparation, visualization, dimensionality reduction, modeling, and the application of machine learning algorithms on real datasets.

SMI1002Databases

Databases II

Advanced database course focused on the technical and internal aspects of database management systems. It helped me better understand storage, indexing, query processing, transaction management, concurrency, security, and the links between databases, web applications, and data analysis.

Fall 2025

5 courses
INF1010Networks

Computer Networks II

Advanced computer networks course focused on Internet technologies, client/server architectures, network administration, and security. It helped me study the TCP/IP model, routing, sockets, VLANs, as well as information security principles and enterprise network design.

INF1014Law

Legal Aspects of Computing

Introductory course on the legal dimensions of computing, software, and the Internet. It helped me understand the main laws and issues related to intellectual property, contracts, personal data protection, software licensing, domain names, and electronic commerce.

SDD1003Data

Data Warehouse Management and Mobile Programming

Course focused on non-relational databases, large-scale data modeling, and their use in web or mobile applications. It helped me understand the differences between relational and NoSQL databases, use MongoDB, model non-normalized schemas, and manipulate large-scale data from a data-oriented perspective.

TIN1003Society

Science, Technology and Society

Critical thinking course on the relationships between science, technology, and society. It helped me examine the social, ethical, and philosophical impacts of scientific and technological innovation, analyze issues such as social responsibility and the viability of new technologies, and develop structured thinking on contemporary dilemmas related to STEM and artificial intelligence.

INF1011Software

Software Engineering

Course focused on the fundamental principles of software engineering and the production of quality software. It helped me deepen my understanding of reusable and maintainable design, quality assurance, testing, software project management, and software evolution, while applying design patterns and design principles in a session project.

Academic foundation

Applied mathematics

A rigorous initial training in analysis, algebra, probability, statistics, differential equations, geometry, and scientific computing.

Path evolution

Transition into computer science

This mathematical foundation naturally supported my development in algorithms, programming, data science, systems, databases, and software design.

Current positioning

Hybrid mathematics + computing profile

My path combines abstract reasoning, quantitative modeling, and concrete execution across software, data, and digital platform projects.

Previous academic background

Previous degree and academic equivalency

Before starting my program at UQTR, I completed a full degree in applied mathematics at the University of Dschang. This training gave me a strong theoretical foundation in modeling, scientific computing, analysis, probability, statistics, and abstract reasoning.

University of DschangCameroon2019 — 2023

Bachelor’s Degree in Applied Mathematics

Complete training in applied mathematics covering algebra, analysis, probability, statistics, differential equations, numerical analysis, topology, geometry, computing, and several scientific applications. This program gave me a rigorous theoretical foundation, strong abstraction skills, and solid abilities in mathematical modeling, scientific computing, and quantitative analysis.

WES academic equivalency

WES evaluation: Canadian equivalency recognized as a Bachelor’s degree (four years).

Year 1

LMD Semester 1

Courses taken

  • MAT111Algebra I: Fundamental Algebraic Concepts
  • MAT121Analysis I: Analysis of the Real Vector Line
  • MAT131Introduction to Computer Science
  • MAT141Vector Analysis
  • MAT151aEnglish Language I
  • MAT161Mechanics I

Advanced concepts covered

  • Foundations of algebra and mathematical reasoning: sets, relations, mappings, operations, and first algebraic structures.
  • Study of real-valued functions of one real variable: limits, continuity, variations, and analytical reading of function behavior.
  • First tools in geometry and vector analysis: vectors, vector operations, and geometric interpretation in the plane and space.
  • Introduction to computing concepts: information representation, processing logic, and fundamentals of algorithms and programming.
  • Mathematical applications to classical mechanics: kinematics, dynamics, and modeling of simple physical phenomena.
  • Development of scientific English vocabulary for academic reading and communication.
LMD Semester 2

Courses taken

  • MAT112Algebra II: Linear Algebra
  • MAT122Analysis II: Differential Calculus
  • MAT132Integral Calculus and Ordinary Differential Equations
  • MAT142Statistics I
  • MAT152Introduction to Algorithms and Programming
  • MAT162Electrostatics

Advanced concepts covered

  • Linear algebra: matrices, determinants, linear systems, vector spaces, bases, dimension, and linear mappings.
  • Differential calculus: differentiation, fundamental theorems, local and global study of functions, and elementary optimization.
  • Integral calculus: antiderivatives, definite integrals, integration techniques, and geometric and physical interpretations.
  • Ordinary differential equations: solving first-order equations and selected simple linear models.
  • Descriptive statistics: organizing, summarizing, and interpreting quantitative data using core indicators.
  • Algorithms and programming: variables, control structures, arrays, functions, logical problem decomposition, and writing simple programs.
  • Physical applications through electrostatics and mathematical modeling of elementary electrical phenomena.

Year 2

LMD Semester 3

Courses taken

  • MAT211Linear Algebra II
  • MAT221Analysis III: Metric Spaces and Series
  • MAT231Differential Calculus on ℝn
  • MAT241Probability Calculation
  • MAT261Theory of Behavior
  • MAT251aEnglish Language II

Advanced concepts covered

  • Advanced linear algebra: reduction of endomorphisms, eigenvalues, eigenvectors, and the structure of linear transformations.
  • Metric spaces and convergence: distance, neighborhoods, sequences, completeness, and the first abstract frameworks of modern analysis.
  • Numerical series and asymptotic behavior useful in analysis and approximation.
  • Differential calculus on ℝn: multivariable functions, partial derivatives, gradient, differential, and extrema.
  • Probability: probability spaces, random variables, common distributions, expectation, variance, and independence.
  • Analytical or modeling-oriented study of behavior depending on the local program content.
  • Strengthening scientific and academic English.
LMD Semester 4

Courses taken

  • MAT212Linear Algebra III
  • MAT222Analysis IV: Integral Calculus on ℝn
  • MAT232Scientific Computation
  • MAT242Computer Architectures
  • MAT252Statistics II
  • MAT272Market Theory

Advanced concepts covered

  • Advanced linear algebra: deeper vector structures, reduction, matrix interpretation, and abstract computational tools.
  • Integral calculus on ℝn: multiple integrals, integration domains, change of variables, and geometric interpretations.
  • Scientific computing and numerical methods: approximation, numerical equation solving, stability, and error.
  • Computer architecture: information representation, memory, processor, hardware organization, and machine logic.
  • Statistics II: deeper inference, estimation, and quantitative data analysis.
  • Market theory: quantitative reading and modeling of economic or decision mechanisms depending on the track followed.

Year 3

LMD Semester 5

Courses taken

  • MAT311General Topology
  • MAT321Groups and Rings
  • MAT331Affine and Projective Geometry
  • MAT341Measure and Integration
  • MAT351Differential Equations
  • MAT361aEnglish Language III

Advanced concepts covered

  • General topology: open and closed sets, continuity, compactness, connectedness, and the abstract language of modern analysis.
  • Abstract algebra: groups, rings, morphisms, and the core structures of modern algebra.
  • Affine and projective geometry: transformations, invariants, and structured geometric modeling.
  • Measure and integration: measurable functions, the framework of modern integration, and advanced tools useful in analysis and probability.
  • Advanced differential equations: systems, qualitative behavior, and continuous modeling.
  • Advanced scientific English for reading and communicating academic content.
LMD Semester 6

Courses taken

  • MAT312Differential Calculus
  • MAT322Complex Variables
  • MAT332Introduction to Differential Geometry
  • MAT342Numerical Analysis
  • MAT352Set Theory
  • MAT362Financial Mathematics

Advanced concepts covered

  • Advanced differential calculus and deeper analytical tools useful for modeling and optimization.
  • Complex variables: complex numbers, holomorphic functions, and the foundations of complex analysis.
  • Differential geometry: local study of curves, surfaces, and differentiable objects.
  • Numerical analysis: discretization, approximation, numerical solving, convergence, and stability of methods.
  • Set theory: formal foundations of mathematics, relations, functions, and set-based structures.
  • Financial mathematics: discounting, interest, annuities, valuation, and quantitative financial modeling.
Professional path

Professional experience and field work

Alongside my academic journey, I have held practical roles in professional environments, both in IT support and data analysis.

Lucatex SARLRemote — Guinea-BissauMarch 2023 — July 2023

Data Analyst Intern

Data analysis internship focused on business data exploration, statistical analysis, and the creation of interactive dashboards to support data-driven decision making.

Main responsibilities

  • Analyzed sales data and trends using Python (pandas, matplotlib).
  • Optimized inventory management based on data analysis.
  • Developed interactive dashboards and reports using Tableau.
  • Built statistical sales forecasts using regression models.
  • Documented analyses and communicated insights to stakeholders.

Technologies / environment

PythonPandasMatplotlibTableauStatistics
Merveille PressinsCameroon2019 — 2023

IT Technician

Responsible for technical support, maintenance of IT equipment, and assisting users in a professional computing environment.

Main responsibilities

  • Installed, configured, and maintained computer hardware and software.
  • Provided technical support, troubleshooting, and incident resolution.
  • Trained and assisted users with IT tools and systems.
  • Managed IT environments and provided operational support.

Technologies / environment

WindowsNetworkingHardware MaintenanceTechnical Support